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larger decrease » marked decrease (Expand Search)
mae decrease » marked decrease (Expand Search), rate decreased (Expand Search)
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shows mae » shows a (Expand Search), show me (Expand Search)
larger decrease » marked decrease (Expand Search)
mae decrease » marked decrease (Expand Search), rate decreased (Expand Search)
_ decrease » _ decreased (Expand Search), _ decreasing (Expand Search)
shows mae » shows a (Expand Search), show me (Expand Search)
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Candidates from the RF method. The top 25 random forest candidates ranked by mean decrease in accuracy and mean decrease in Gini Index are in the first and second columns, respectively....
Published 2025“…The top 25 random forest candidates ranked by mean decrease in accuracy and mean decrease in Gini Index are in the first and second columns, respectively. …”
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Mean 0-30 m sprint times for TSG, VBT, and CG groups at pre-test and post-test (with 95% CI).
Published 2025Subjects: -
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Mean 0-20 m sprint times for TSG, VBT, and CG groups at pre-test and post-test (with 95% CI).
Published 2025Subjects: -
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Mean 0-10 m sprint times for TSG, VBT, and CG groups at pre-test and post-test (with 95% CI).
Published 2025Subjects: -
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Fluctuation trend of the mean temperature index.
Published 2025“…Thirdly, the relevant e high temperature indices of plain urban area were larger while the relevant low temperature indices of mountain hilly area were smaller. …”
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Variation curve of the mean temperature index.
Published 2025“…Thirdly, the relevant e high temperature indices of plain urban area were larger while the relevant low temperature indices of mountain hilly area were smaller. …”
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Mann-Kendall test for the mean temperature index.
Published 2025“…Thirdly, the relevant e high temperature indices of plain urban area were larger while the relevant low temperature indices of mountain hilly area were smaller. …”
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The MAE value of the model under raw data.
Published 2025“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”
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Quantification of ingested proteins by females after 30 minutes of blood feeding.
Published 2025Subjects: